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Automatic Smile Recognition From Face Images 23

Automatic Smile Recognition From Face Images

引用

23nd Signal Processing and Communications Applications Conference (SIU)

作者： Akkoca, Bilge Suheyla Gokmen, Muhittin Istanbul Tech Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781467373869

Smile detection is commonly used in many devices with recent technology. For this aim, we deal with recognition of smile expression automatically. In this paper, face images with smile and neutral expression are selected from various data sets. local binary patterns and Global Zernike Moments, local Zernike Moments and Global Zernike Moments, local Zernike Moments, local XOR patterns and Global Zernike Moments methods are applied and produced the results of them. Also, the results are shown comparatively.

关键词： Smile recognition local Zernike Moments Global Zernike Moments local XOR patterns local binary patterns

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Fault Diagnosis of Analog Circuit Using Spectrogram And LVQ Neural Network 27

Fault Diagnosis of Analog Circuit Using Spectrogram And LVQ ...

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27th Chinese Control and Decision Conference (CCDC)

作者： Li, Penghua Zhang, Shunxing Luo, Dechao Luo, Hongping Chongqing Univ Posts & Telecommun Coll Automat Automot Elect Engn Res Ctr Chongqing 400065 Peoples R China Natl Inst Automot Engn Key Lab Vehicle Emiss & Economizing Energy Chongqing 400039 Peoples R China

ISBN: (纸本)9781479970162

This paper addresses a refined fault feature problem of analog circuit using a feature extraction technique based on auditory feature. The proposed approach applies short-time fernier transform (STFT) to obtain the time and frequency features of the fault responses being indicated separately by the cross and vertical axes in a spectrogram, which gives much more refined description of the fault behavior. To reduce the computational complexity derived from the high-dimensional texture features embedded in the spectrogram, the fault spectrograms are further processed by local binary patterns (LBP) operator for obtaining low-dimensional fault features. Completing the parameter settings of the network, the LBP feature vectors are fed to the learning vector quantization (LVQ) neural network for fault classification. The numerical experiments about an active high-pass filter are carried out to indicate our approach has an acceptable diagnostic rate with high accuracy.

关键词： Analog Fault Diagnosis Spectrogram Learning Vector Quantization local binary patterns

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Texture Classification Using Rotation Invariant LBP Based on Digital Polygons 18th

Texture Classification Using Rotation Invariant LBP Based on...

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18th International Conference on Image Analysis and Processing (ICIAP)

作者： Pardo-Balado, Juan Fernandez, Antonio Bianconi, Francesco Univ Vigo Sch Ind Engn Vigo 36310 Spain Univ Perugia Dept Engn I-06125 Perugia Italy

ISBN: (纸本)9783319232225;9783319232218

This paper investigates the use of digital polygons as a replacement for circular interpolated neighbourhoods for extracting texture features through local binary patterns. The use of digital polygons has two main advantages: reduces the computational cost, and avoids the high-frequency loss resulting from pixel interpolation. The solution proposed in this work employs a sub-sampling scheme over Andres' digital circles. The effectiveness of the method was evaluated in a supervised texture classification experiment over eight different datasets. The results showed that digital polygons outperformed interpolated circular neighbourhoods in most cases.

关键词： local binary patterns Texture classification Digital circles Digital polygons Rotation invariance

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COMPARISON OF TWO METHODS FOR DETECTION OF NORTH ATLANTIC RIGHT WHALE UPCALLS 23

COMPARISON OF TWO METHODS FOR DETECTION OF NORTH ATLANTIC RI...

引用

23rd European Signal Processing Conference (EUSIPCO)

作者： Esfahanian, Mahdi Zhuang, Hanqi Erdol, Nurgun Gerstein, Edmund Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA

ISBN: (纸本)9780992862633

In this paper, a study is carried out for detecting North Atlantic Right Whale upcalls with measurements from passive acoustic monitoring devices. Preprocessed spectrograms of upcalls are subjected to two different tasks, one of which is based on extraction of time-frequency features from upcall contours, and the other that employs a local binary Pattern operator to extract salient texture features of the upcalls. Then several classifiers are used to evaluate the effectiveness of both the contour-based and texture-based features for upcall detection. Detection results reveal that popular classifiers such as Linear Discriminant Analysis, Support Vector Machine, and TreeBagger can achieve high detection rates. Furthermore, using LBP features for call detection shows improved accuracy of about 3% to 4% over time-frequency features when an identical classifier is used.

关键词： North Atlantic Right Whale local binary patterns Spectral Denoising Upcall Detection

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Facial Expression Recognition Using Support Vector Machines 23

Facial Expression Recognition Using Support Vector Machines

引用

23nd Signal Processing and Communications Applications Conference (SIU)

作者： Abdulrahman, Muzammil Eleyan, Alaa Mevlana Univ Konya Dept Elect & Elect Engn Konya Turkey

ISBN: (纸本)9781467373869

This paper propose a facial expression recognition approach based on Principal Component Analysis (PCA) and local binary Pattern (LBP) algorithms. Experiments were carried out on the Japanese Female Facial Expression (JAFFE) database and our recently introduced Mevlana University Facial Expression (MUFE) database. Support Vector Machine (SVM) was used as classifier. In all conducted experiments on JAFFE and MUFE databases, obtained results reveal that PCA+SVM has an average recognition rate of 87% and 77%, respectively.

关键词： Facial Expression Recognition local binary patterns Principal Component Analysis Support Vector Machine

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LEARNING DISCRIMINATIVE local FEATURES FROM IMAGE-LEVEL LABELLED DATA FOR COLONOSCOPY IMAGE CLASSIFICATION 12

LEARNING DISCRIMINATIVE LOCAL FEATURES FROM IMAGE-LEVEL LABE...

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IEEE 12th International Symposium on Biomedical Imaging

作者： Manivannan, Siyamalan Trucco, Emanuele Univ Dundee Sch Comp CVIP Comp Vis & Image Proc Grp Dundee DD1 4HN Scotland

ISBN: (纸本)9781479923748

In this paper we propose a novel weakly-supervised feature learning approach, learning discriminative local features from image-level labelled data for image classification. Unlike existing feature learning approaches which assume that a set of additional data in the form of matching/non-matching pairs of local patches are given for learning the features, our approach only uses the image-level labels which are much easier to obtain. Experiments on a colonoscopy image dataset with 2100 images shows that the learned local features outperforms other hand-crafted features and gives a state-or-the-art classification accuracy of 93.5%.

关键词： Discriminative feature learning local binary patterns Colonoscopy image classification

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Image retrieval using Color and Texture binary patterns 1

Image retrieval using Color and Texture Binary Patterns

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International Conference on Green Computing and Internet of Things (ICGCIoT)

作者： Bhagyalakshmi, A. Chamundeeswari, V. Vijaya Velammal Engn Coll Comp Sci & Engn Madras Tamil Nadu India

ISBN: (纸本)9781467379106

Image retrieval plays a major role in security systems to extract the images with similar features or patterns, to retrieve the relevant images in web search engines, in industries to detect crack in the manufactured parts, in architectural designs to find same texture patterns and so on. To accomplish efficiency in all the fields of image processing, the effective image retrieval mechanism is imminent. In this paper, we proposed a method based on the combination of binary texture patterns and color features. A local binary Pattern (LBP) plays an important role in extracting the binary texture features and color histogram. This feature is implemented to identify and extract features of prominent object present in an image. Using different statistical measures, similarity measures are calculated and evaluated. Image retrieval based on color or texture is a trivial task. Identifying objects of prominence in an image and retrieving image with similar features is a complex task. Finding prominent object in an image is difficult in a background image and is the challenging task in retrieving images. The Implementation results proved that proposed method is effective in recalling the images of same pattern or texture.

关键词： Color histogram local binary patterns Image retrieval

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On the Effects of Illumination Normalization with LBP-Based Watchlist Screening 13

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13th European Conference on Computer Vision (ECCV)

作者： Amara, Ibtihel Granger, Eric Hadid, Abdenour Univ Quebec Ecole Technol Super Montreal PQ H3C 3P8 Canada Univ Oulu Ctr Machine Vis Res Oulu Finland

ISBN: (纸本)9783319161815;9783319161808

Still-to-video face recognition (FR) is an important function in several video surveillance applications like watchlist screening, where faces captured over a network of video cameras are matched against reference stills belonging to target individuals. Screening of faces against a watchlist is a challenging problem due to variations in capturing conditions (e.g., pose and illumination), to camera inter-operability, and to the limited number of reference stills. In holistic approaches to FR, local binary Pattern (LBP) descriptors are often considered to represent facial captures and reference stills. Despite their efficiency, LBP descriptors are known as being sensitive to illumination changes. In this paper, the performance of still-to-video FR is compared when different passive illumination normalization techniques are applied prior to LBP feature extraction. This study focuses on representative retinex, self-quotient, diffusion, filtering, means de-noising, retina, wavelet and frequency-based techniques that are suitable for fast and accurate face screening. Experimental results obtained with videos from the Chokepoint dataset indicate that, although Multi-Scale Weberfaces and Tan and Triggs techniques tend to outperform others, the benefits of these techniques varies considerably according to the individual and illumination conditions. Results suggest that a combination of these techniques should be selected dynamically based on changing capture conditions.

关键词： Illumination normalization local binary patterns Face screening Still-to-video face recognition Video surveillance

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Lightweight binary voxel shape features for 3D data matching and retrieval 1

Lightweight binary voxel shape features for 3D data matching...

引用

IEEE First International Conference on Multimedia Big Data

作者： Matsuda, Takahiro Furuya, Takahiko Ohbuchi, Ryutarou Univ Yamanashi Grad Sch Med & Engn Kofu Yamanashi Japan

ISBN: (纸本)9781479986880

This paper proposes several lightweight local 3D shape features for 3D voxel data that yield compact binary feature vectors. These features are inspired by compact binary features for 2D image, namely, local binary Pattern (LBP) [22], BRIEF [6] and ORB [26]. In addition to being compact, extraction of proposed 3D features is inexpensive. Furthermore, these binary feature vectors are very efficient to compare, as their distance in Hamming space can be computed very efficiently. Our experimental evaluation of these features in a shape-based 3D model retrieval setting showed that some of these 3D binary features perform competitively to some of existing features. Depending on benchmark database, proposed features are somewhat less accurate than or about as accurate as the state-of-the-art 3D shape features. However, memory footprint is much more compact, at about 1/10 of the non-binary 3D shape features having comparable retrieval accuracy.

关键词： 3D geometric models 3D shape comparison local feature binary feature local binary patterns BRIEF ORB

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Robust Facial Expression Classification Using Shape and Appearance Features 8

Robust Facial Expression Classification Using Shape and Appe...

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8th International Conference on Advances in Pattern Recognition (ICAPR)

作者： Happy, S. L. Routray, Aurobinda Indian Inst Technol Dept Elect Engn Kharagpur W Bengal India

ISBN: (纸本)9781479974580

Facial expression recognition has many potential applications which has attracted the attention of researchers in the last decade. Feature extraction is one important step in expression analysis which contributes toward fast and accurate expression recognition. This paper represents an approach of combining the shape and appearance features to form a hybrid feature vector. We have extracted Pyramid of Histogram of Gradients (PHOG) as shape descriptors and local binary patterns (LBP) as appearance features. The proposed framework involves a novel approach of extracting hybrid features from active facial patches. The active facial patches are located on the face regions which undergo a major change during different expressions. After detection of facial landmarks, the active patches are localized and hybrid features are calculated from these patches. The use of small parts of face instead of the whole face for extracting features reduces the computational cost and prevents the over-fitting of the features for classification. By using linear discriminant analysis, the dimensionality of the feature is reduced which is further classified by using the support vector machine (SVM). The experimental results on two publicly available databases show promising accuracy in recognizing all expression classes.

关键词： Facial expression recognition active facial patch Linear Discriminant Analysis local binary patterns Pyramid of Histogram of Gradient Support Vector Machine

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